Statistical data analysis multiple choice
If sales is the variable you are trying to explain and you have 2 independent variables of color and price. The color coefficient is 5, and the price coefficient is 20. You have an intercept coefficient of 500 and an rsquared value of .2500. Using this multiple regression analysis, predict the amount of sales with a color rank of 5 and a price of 20 dollars.
[removed] A. The sales will be between 0 and 100 

[removed] B. The sales will be between 101 and 200 

[removed] C. The sales will be between 201 and 800 

[removed] D. The sales will be above 801 

[removed] E. You will actually have negative sales using this multiple regression analysis 
In multiple regression analysis you can have 2 or more independent variables.

If sales is the variable you are trying to explain and you have 3 independent variables of video marketing, radio marketing, and price. The video marketing coefficient is 100, the radio marketing coefficient is 20, and the price coefficient is 20. You have an intercept coefficient of 500 and an rsquared value of .6500. Using this multiple regression analysis, predict the amount of sales with 500 dollars being spent on video marketing, 500 dollars being spent on radio marketing, and the price is 100 dollars.
[removed] A. The sales will be between 0 and 4,800 

[removed] B. The sales will be between 4,801 and 6,000 

[removed] C. The sales will be between 6,001 and 8,000 

[removed] D. The sales will be above 8,001 

[removed] E. You will actually have negative sales using this multiple regression analysis 
If sales is the variable you are trying to explain and you have 2 independent variables of color and price. The color coefficient is 50, and the price coefficient is 20. You have an intercept coefficient of 5000 and an rsquared value of .2500. Using this multiple regression analysis, predict the amount of sales with a color rank of 5 and a price of 200 dollars.
[removed] A. The sales will be between 0 and 500 

[removed] B. The sales will be between 501 and 600 

[removed] C. The sales will be between 601 and 800 

[removed] D. The sales will be above 801 

[removed] E. You will actually have negative sales using this multiple regression analysis 
The general purpose of multiple regression is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable.

If the sum of squares regression is 100 and the sum of squares total is 500. Given that the sum of squares residual (or error) is 400 what is the rsquared value?
[removed] A. lower than .25 

[removed] B. between .26 and .5 

[removed] C. between .51 and .75 

[removed] D. between .76 and 1.00 

[removed] E. greater than 1.01 
The rsquared never decreases when a new variable is added to the regression equation.

Which of the following statements is NOT true?
[removed] A. adjusted rsquared is always smaller than the r squared value 

[removed] B. the adjusted rsquared value will always decrease with the addition of another variable 

[removed] C. the rsquared value cannot decrease when a new variable is added to the regression. 

[removed] D. the adjusted rsquared, more so than the rsquared, is more useful for comparing different regression models. 

[removed] E. All of the above 
You are trying to interpret a ttest for your multiple regression analysis. You had 10 total observations with 3 independent variables. The regression on the independent variables returned coefficients and tstats as follows ? Price coefficient: 10 and t stat is 7. Location coefficient: 2 and t stat is 3. Advertising coefficient is 100 and t stat is 1.0. Which of the following statements is an accurate interpretation of the situation when using an alpha of .10?
[removed] A. Only the price and location variables are significant. 

[removed] B. Our output goes down 10 for every 1 dollar increase in price. 

[removed] C. The critical value you should use is 1.943 and 1.943 

[removed] D. All of the above 

[removed] E. None of the above. 
You are trying to interpret a ttest for your multiple regression analysis. You had 25 total observations with 2 independent variables. The regression on the independent variables returned coefficients and tstats as follows ? Price coefficient: 100 and t stat is 1.75. Location coefficient: 2 and t stat is 3.5. Which of the following statements is an accurate interpretation of the situation when using an alpha of .05?
[removed] A. Only the location variable is significant. 

[removed] B. Our output goes down 100 for every 1 dollar increase in price. 

[removed] C. The critical value you should use is 2.074 and 2.074 

[removed] D. All of the above 

[removed] E. None of the above. 
A dummy variable is a continuous numerical variable.

In multiple regression analysis, a dummy variable is one that takes the values 0 or 1 to indicate the absence or presence of some categorical effect. If the coefficient on a dummy variable is 15 that means that if the variable is present, we should expect our depedent variable to increase by 15.

Compare two models. Model #1 has an rsquared of .55 and an adjusted rsquared of .50. Model #2 has an rsquared of .52 and an adjusted rsquared of .51. Given this information, what model should you use?
[removed] A. Use model 1 because rsquared is higher 

[removed] B. Use model 2 because adjusted rsquared is higher 

[removed] C. Use model 1 because the combination of rsquared and adjusted rsquared is higher 

[removed] D. Use model 2 because the combination of rsquared and adjusted rsquared is lower 

[removed] E. Use model 1 because the adjusted rsquared is lower 
Compare two models. Model A has an rsquared of .85 and an adjusted rsquared of .60. Model B has an rsquared of .76 and an adjusted rsquared of .63. Given this information, what model should you use?
[removed] A. Use model A because the adjusted rsquared is lower. 

[removed] B. Use model B because the rsquared is higher 

[removed] C. Use model A because the rsquared is higher than the adjusted rsquared of model B 

[removed] D. Use model B because the combination of rsquared and adjusted rsquared is higher 

[removed] E. Use model B because the adjusted rsquared is higher 
Compare 2 models. Model Q has an rsquared of .90 and an adjusted rsquared of .80. Model Z has an rsquared of .94 and an adjusted rsquared of .82. Given this information, what model should you choose?
[removed] A. Use model Z because adjusted rsquared is higher 

[removed] B. Use model Q because rsquared is lower 

[removed] C. Use model Q because the combination of rsquared and adjusted rsquared is lower 

[removed] D. Use model Z because the combination of rsquared and adjusted rsquared is higher 

[removed] E. Use model Q because the adjusted rsquared is lower 
The independent variable is the variable you wish to explain.

You are a realtor with a small business. You use a simple 2 variable linear regression analysis for quick first glance house price estimates using square footage of the house. You are asked by a customer what the price for his house would be given that his house has 1,500 square feet. The number you are trying to explain is the potential price of the house. You look at your numbers from his neighborhood and run the regression analysis. Your program returns the following data: R square: .5808, Square footage (X) Coefficient: 95.0, Observations: 20, Degrees of Freedom: 19, Intercept tstat: 1.87, Multiple R: .7723, Intercept Coefficient: 120,200, Square footage (X) Standard Error: .0288. What is the best first glance prediction you can give the customer about the value of his house given your 2 variable linear regression analysis?
[removed] A. 0 to 500 

[removed] B. 501 to 100,000 

[removed] C. 100,001 to 200,000 

[removed] D. 200,001 to 400,000 

[removed] E. 400,001 to 1.5 million 
The r2 (rsquared) value is equal to the sum of squares regression divided by ______________.
[removed] A. the sum of squares error 

[removed] B. the sum of squares total 

[removed] C. the sum of squares residual 

[removed] D. the adjusted rsquared 

[removed] E. the degrees of freedom 
Name a L.I.N.E assumption of regression analysis.
[removed] A. Interconnectedness of variance equations 

[removed] B. Nonhyperbolated numerarity. 

[removed] C. Linearity 

[removed] D. Initiated variances 

[removed] E. None of the above 
You are doing regression analysis and your tstat for your independent variable is negative and significant. Thus, you can conlude that you have a linear relationship between the independent and dependent variables.

Graph one shows a high number of observations that are very spread out but generally trending from the top left of the graph to the bottom right. Graph two has very few observations but the few that exist are tightly packed around a trend line sloping from the top left to the bottom right. Which of the following statement(s) are true?
[removed] A. Graph one should have a higher rsquared value 

[removed] B. Graph two should have a higher rsquared value 

[removed] C. Graph one has a negative slope 

[removed] D. Graph two has a negative slope 

[removed] E. B, C, and D 
Graph one shows a number of observations that are very spread out but generally trending from the bottom left of the graph to the top right. Graph two has a number of observations that would be tightly packed around a trend line sloping from the top left to the bottom right. Which of the following statement(s) are true?
[removed] A. Graph one should have a higher rsquared value 

[removed] B. Graph two should have a higher rsquared value 

[removed] C. Graph one has a negative slope 

[removed] D. Graph two has a positive slope 

[removed] E. B, C, and D 
Graph one shows a number of observations that all are directly on the trend line that moves from the top left of the graph to the bottom right. Graph two has a number of observations that would be tightly packed around, but not directly on, a trend line that runs straight across the graph. Which of the following statement(s) are true?
[removed] A. Graph one should have a higher rsquared than graph two 

[removed] B. Graph two should have a higher rsquared than graph one 

[removed] C. Graph one will have a negative rsquared value 

[removed] D. Graph two will have an rsquared value of 0.00 

[removed] E. C and D 
What is your rsquare value given the following information: Sum of Squares Residual (or Error): 1,800, Intercept Coefficient: 50.25, Sum of Squares Regression: 300, Independent Variable Coefficient: .05.
[removed] A. .20 or below 

[removed] B. .21 to .40 

[removed] C. .41 to .60 

[removed] D. .61 to .80 

[removed] E. .81 and above 
You have 4 observed values of the dependent variable in your sample. The values are 3, 6, 9, and 12. Calculate the SST.
[removed] A. The SST is between 0 and 50 

[removed] B. The SST is between 51 and 100 

[removed] C. The SST is between 101 and 200 

[removed] D. Above 200 

[removed] E. Not enough information is provided 
Which of the following terms describes the overall longterm tendency of a time series?
[removed] A. cyclical component 

[removed] B. trend 

[removed] C. irregular 

[removed] D. seasonal 

[removed] E. B and C 
Timeseries models should NOT be used to mechanically extrapolate trends into the future without considering personal judgments, business experiences, changing technologies, etc.

Given a data set with 30 yearly observations, there are only 26 fiveyear moving averages.

The method of moving averages is used:
[removed] A. to smooth a series 

[removed] B. to plot a series 

[removed] C. to exponentiate a sereies 

[removed] D. B and C 

[removed] E. in regression analysis. 
The method of least squares is used on timeseries data for:
[removed] A. eliminating irregular movements. 

[removed] B. deseasonalizing the data. 

[removed] C. exponentially smoothing a series. 

[removed] D. A and C 

[removed] E. obtaining the trend equation. 
To assess the adequacy of a forecasting model, one measure that is often used is:
[removed] A. quadratic trend analysis. 

[removed] B. exponential smoothing. 

[removed] C. moving averages. 

[removed] D. the Mean Absolute Deviations (MAD). 

[removed] E. None of the above 
Which of the following is a guideline for selecting a model for forecast?
[removed] A. Measure the magnitude of the residual error through standard error of the estimate or mean absolute deviation. 

[removed] B. Perform a residual analysis. 

[removed] C. Use the principle of parsimony. 

[removed] D. All of the above. 

[removed] E. A and C only. 
Forecasting is done to monitor changes that occur over ___________.
[removed] A. component factors 

[removed] B. cyclical factors 

[removed] C. irregular factors 

[removed] D. time 

[removed] E. none of the above 
The cyclical component of a time series:
[removed] A. represents periodic fluctuations which recur within one year. 

[removed] B. is obtained by adding up the seasonal indexes. 

[removed] C. A and B 

[removed] D. is obtained by adjusting for calendar variation. 

[removed] E. represents periodic fluctuations which usually occur in two to ten years. 